Abstract

This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also includemore » a modest set of spectroscopic data sufficient to train neural networks.« less

@article{osti_22273350,
title = {DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS},
author = {Dong, X. Y. and De Robertis, M. M., E-mail: xydong@yorku.ca},
abstractNote = {This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.},
doi = {10.1088/0004-6256/146/4/87},
journal = {Astronomical Journal (New York, N.Y. Online)},
number = 4,
volume = 146,
place = {United States},
year = 2013,
month =
}

An artificial neural network (ANN)-based diagnostic adviser capable of identifying the operating status of a nuclear power plant is described. A dynamic node architecture scheme is used to optimize the architectures of the two backpropagation ANNs that embody the advisor. The first or root network is used to determine whether or not the plant is in a normal operating condition. If the plant is not in a normal condition, the second or classifier network is used to recognize the particular off-normal condition or transient taking place. These networks are developed using simulated plant behavior during both normal and abnormal conditions.more » The adviser is effective at diagnosing 27 distinct transients based on 43 scenarios simulated at various severities that contain up to 3% noise.« less

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We used photometry from the Kepler satellite to characterize the variability of four radio-loud active galactic nuclei (AGNs) on timescales from years to minutes. The Kepler satellite produced nearly continuous high precision data sets which provided better temporal coverage than possible with ground based observations. We have now accumulated 11 quarters of data, eight of which were reported in our previous paper. In addition to constructing power spectral densities (PSDs) and characterizing the variability of the last three quarters, we have linked together the individual quarters using a multiplicative scaling process, providing data sets spanning ∼2.8 yr with >98% coveragemore » at a 30 minute sampling rate. We compute PSDs on these connected data sets that yield power law slopes at low frequencies in the approximate range of –1.5 to –2.0, with white noise seen at higher frequencies. These PSDs are similar to those of both the individual quarters and to those of ground-based optical observations of other AGNs. We also have explored a PSD binning method intended to reduce a bias toward shallow slope fits by evenly distributing the points within the PSDs. This tends to steepen the computed PSD slopes, especially when the low frequencies are relatively poorly fit. We detected flares lasting several days in which the brightness increased by ∼15%-20% in one object, as well a smaller flare in another. Two AGNs showed only small, ∼1%-2%, fluctuations in brightness.« less

Neural network modeling is a powerful nonlinear regression analysis method that is extremely useful in identifying behavioral trends. This methodology was applied to the problem of predicting Ferrite Number in arc welds as a function of composition. This paper describes the details of the development of the neural network model, named FNN-1999, including the identification of the optimum network architecture and network parameters. The model was trained on the same data as the WRC-1992 constitution diagram and covers a range of Ferrite Numbers from 0 to 117, with a corresponding wide range in composition. Results of the model are presentedmore » in Part 2. It is shown that the accuracy of the FNN-1999 model in predicting Ferrite Number is superior to the accuracy of other models that are currently available, including the WRC-1992 diagram.« less